====== Fact Checking ======

===== Automatic Fact Checking =====

==== Overviews ====
See [[https://www.aclweb.org/anthology/search/?q=automatic+fact+checking|ACL Anthology - Automatic Fact Checking]].
  * [[https://www.aclweb.org/anthology/C18-1283.pdf|Thorne & Vlachos 2018 - Automated Fact Checking: Task Formulations, Methods and Future Directions]]
  * Popular media: [[https://www.reutersagency.com/wp-content/uploads/2019/03/reuters-institute-graves-factsheet-180228.pdf|2019 - Understanding the Promise and
Limits of Automated Fact-Checking]]
  * [[https://www.aclweb.org/anthology/2020.coling-main.474.pdf|Kotonya & Toni 2020 - Explainable Automated Fact-Checking: A Survey]]
  * [[https://arxiv.org/pdf/2108.11896|Gu et al 2021 - A Survey on Automated Fact-Checking]]
  * [[https://arxiv.org/pdf/2301.03056.pdf|Das et al 2023 - The State of Human-centered NLP Technology for Fact-checking]]

==== Papers ====
  * [[https://arxiv.org/pdf/1803.05355.pdf|Thorne et al 2018 - FEVER: A Large-Scale Dataset for Fact Extraction and VERification]]
  * **[[https://arxiv.org/pdf/2204.05511.pdf|Chen et al 2022 - GERE: Generative Evidence Retrieval for Fact Verification]]**
  * **[[https://arxiv.org/pdf/2305.13117|Schlichtkrull et al 2023 - AVERITEC: A Dataset for Real-world Claim Verification with Evidence from the Web]]**
  * Fact checking against tables: [[https://arxiv.org/pdf/1909.02164.pdf|Chen at el 2019 - TabFact: A Large-scale Dataset for Table-based Fact Verification]]
    * [[https://arxiv.org/pdf/2004.13659.pdf|Zhong et al 2020 - LogicalFactChecker: Leveraging Logical Operations for Fact Checking with Graph Module Network]]
    * [[https://aclanthology.org/2022.acl-long.525.pdf|Ou & Liu 2022 - Learning to Generate Programs for Table Fact Verification via Structure-Aware Semantic Parsing]]
  * Claims verification
    * [[https://aclanthology.org/2022.acl-long.92.pdf|Khan et al 2022 - WatClaimCheck: A new Dataset for Claim Entailment and Inference]]

===== Fake News Detection =====
  * **Overviews**
    * [[https://arxiv.org/pdf/1811.00770.pdf|Oshikawa et al 2018 - A Survey on Natural Language Processing for Fake News Detection]]
  * GROVER: [[https://arxiv.org/pdf/1905.12616.pdf|Zellers et al 2019 - Defending Against Neural Fake News]]
  * [[https://arxiv.org/pdf/1911.03854.pdf|Nakamura et al 2019 - Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection]]
  * Fung et al 2021 - InfoSurgeon: Cross-Media Fine-grained Information Consistency Checking for Fake News Detection

===== Methods for Enhancing Factuality =====
  * **In Language Models**
    * [[https://arxiv.org/pdf/2206.04624.pdf|Lee et al 2022 - Factuality Enhanced Language Models for Open-Ended Text Generation]]

===== Datasets =====
  * [[https://fever.ai/resources.html|FEVER]]: [[https://arxiv.org/pdf/1803.05355.pdf|Paper]], [[https://github.com/awslabs/fever|GitHub]]

===== Workshops and Shared Tasks =====
  * [[https://fever.ai/|Workshop on Fact Extraction and Verification (FEVER)]]

===== People =====
  * [[https://scholar.google.com/citations?user=DfXsKZ4AAAAJ&hl=en|Preslav Nakov]]
  * [[https://scholar.google.com/citations?user=XjWnyM4AAAAJ&hl=en|Andreas Vlachos]]

===== Related Pages =====
  * [[Factivity]]
  * [[Hallucination and Factivity]]